Joint embedding of corpus pairs for domain mapping

ABSTRACT

Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding. The scoring the joint embedding affinity comprises: transforming the embedded representation of the first keyword and the embedded representation of the second keywords via the trained model; determining an affinity value based on comparing the first keyword to the second keywords; and based on the affinity value, aggregating the joint embedding of the embedded representation of the first keyword and the embedded representation of the second keywords within the second domain corpus.

BACKGROUND

The subject disclosure relates to corpus pairs, and more specifically,to mapping of corpus pairs.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the disclosure. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, devices, systems, computer-implemented methods,apparatus and/or computer program products that facilitate outside-inmapping for corpus pairs are described.

According to an embodiment, a computer program product for managing amapping process can comprise a computer readable storage medium havingprogram instructions embodied therewith. The program instructions can beexecutable by a processor and the processor can execute a first portionof a thread of execution. The program instructions can also beexecutable to input a plurality of first keywords associated with afirst domain corpus, and extract a first keyword of the plurality offirst keywords. The program instructions can also be executable to inputof a plurality of second keywords associated with a second domaincorpus, generate a first embedded representation of the first keywordvia a trained model, and generate a second embedded representation ofthe second keywords via the trained model. The program instructions canalso be executable to score a joint embedding affinity associated with ajoint embedding, wherein the scoring the joint embedding affinitycomprises: transforming the first embedded representation of the firstkeyword and the second embedded representation of the second keywordsvia the trained model, and determining an affinity value based oncomparing the first keyword to the second keywords. Based on theaffinity value, scoring the joint embedding affinity comprisesaggregating the joint embedding of the first embedded representation ofthe first keyword and the second embedded representation of the secondkeywords within the second domain corpus.

According to another embodiment, a computer-implemented method isprovided. The computer-implemented method can comprise analyzing, by adevice operatively coupled to a processor, first domain data associatedwith a domain comprising a first corpus, resulting in first analyzeddata. The computer-implemented method can also comprise analyzing, bythe device, second domain data associated with a second domaincomprising a second corpus, resulting in second analyzed data. Based onthe analyzing the first domain data and the analyzing the second domaindata, the computer-implemented method can comprise identifying, by thedevice, equivalent terms between the first domain data and the seconddomain data. Additionally, based on the equivalent terms, the firstanalyzed data, and the second analyzed data, the computer-implementedmethod can comprise jointly embedding, by the device, the first domaindata and the second domain data, resulting in jointly embedded data; andin response to the jointly embedding, the computer-implemented methodcan comprise outputting, by the device, a model associated with thejointly embedded data.

According to yet another embodiment, a computer-implemented method isprovided. The computer-implemented method can comprise generating, by adevice operatively coupled to a processor, a first embeddedrepresentation of a profile term of the first terms, associated with auser identity profile, via a trained model, wherein the first terms areassociated with the user identity profile of a first domain corpus. Thecomputer-implemented method can also comprise generating, by the device,a second embedded representation of second terms, associated a seconddomain corpus, via the trained model. Additionally, thecomputer-implemented method can also comprise comparing, by the device,the profile term to the second terms to determine an affinity valuebased on a joint embedding of the first embedded representation and thesecond embedded representation, resulting in a comparison data. Thecomputer-implemented method can also comprise generating, by the device,based on the affinity value, display data associated with the comparisondata for display by a webpage.

In some embodiments, one or more of the above elements described inconnection with the systems, computer-implemented methods and/orcomputer program programs can be embodied in different forms such as acomputer-implemented method, a computer program product, or a system.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting systemthat facilitates joint embedding of corpus pairs for outside-in mappingin accordance with one or more embodiments described herein.

FIG. 2 illustrates another block diagram of an example, non-limitingsystem that facilitates joint embedding of corpus pairs for outside-inmapping in accordance with one or more embodiments described herein.

FIG. 3 illustrates yet another block diagram of an example, non-limitingsystem that facilitates joint embedding of corpus pairs for outside-inmapping in accordance with one or more embodiments described herein.

FIG. 4 illustrates an additional block diagram of an example,non-limiting system that facilitates joint embedding of corpus pairs foroutside-in mapping in accordance with one or more embodiments describedherein.

FIG. 5 illustrates an embodiment of a joint embedding component inaccordance with one or more embodiments described herein.

FIG. 6 illustrates an embodiment of a content mapping component inaccordance with one or more embodiments described herein.

FIG. 7 illustrates yet another example, non-limiting system thatfacilitates joint embedding of corpus pairs for outside-in mapping inaccordance with one or more embodiments described herein.

FIG. 8 illustrates a flow diagram of an example, non-limitingcomputer-implemented method that facilitates a mapping process inaccordance with one or more embodiments described herein.

FIG. 9 illustrates a flow diagram of another example, non-limitingcomputer-implemented method that facilitates development of a trainingmodel in accordance with one or more embodiments described herein.

FIG. 10 illustrates a flow diagram of another example, non-limitingcomputer-implemented method that facilitates display of data based on anaffinity value associated with a user profile in accordance with one ormore embodiments described herein.

FIG. 11 illustrates a block diagram of an example, non-limitingoperating environment in which one or more embodiments described hereincan be facilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

Companies can leverage search engine optimization (SEO) and keywordexperts to manually curate lists of important keywords and map tocompany tags/content. However, manual data entry can have inherentscaling issues and generate long lead times to optimize the lists andmappings. Commonly related items can have different associatedterminology across various companies or organizations. For instance, byvarious companies, the term “expert systems” can be referred to as“unified computing systems,” the term “cognitive computing” can bereferred to as “artificial intelligence,” and the term “digitalexperience” can be referred to as “customer engagement.” However,effective marketing can require content to be readily searchable andpersonalized.

Outside-in language can mean language that is external to a domaincorpus. Outside-in language be compared to language that is internal tothe domain corpus. Language that is internal to the domain corpus can befound within the domain corpus and language that is external to thedomain corpus may not necessarily be found in the domain corpus.

This disclosure describes systems, computer-implemented methods and/orcomputer program products that can leverage corpus pairs to learnoutside-in term mappings for taxonomies and content in an automated andunsupervised manner (e.g., no labeling of terms are required). Inlinguistics, a “corpus” or “corpora” can mean a set of text (usuallyelectronically stored and processed). As used herein, the terms “corpus”and/or “corpora” can be employed interchangeably as appropriate toindicate one corpus or multiple corpus, respectively. A corpus can beemployed to perform statistical analysis and hypothesis testing and/orvalidation of linguistic rules within a specific language or the like.In various embodiments, a corpus can contain text data in a singlelanguage (monolingual corpus) or text data in multiple languages(multilingual corpus).

Multilingual corpora that have been specially formatted for side-by-sidecomparison are called aligned parallel corpora. There are two main typesof parallel corpora, which contain texts in two languages. In atranslation corpus, the texts in one language are translations of textsin the other language. In a comparable corpus, the texts are of the samekind and cover the same content, but they are not translations of eachother. To exploit a parallel corpus, text alignment identifyingsubstantially equivalent text segments (phrases or sentences) can beemployed to facilitate analysis. It should be noted that any referenceto the term “equivalent” herein can mean substantially equivalent termsand/or synonymous terms.

Different corpora can have different levels of analysis applied. Forexample, some smaller corpora (e.g., treebank corpora or parsedcorpora), that may include one to three million words, can be fullyparsed. Other levels of linguistic structured analysis are possibleincluding annotations, morphology, semantics and/or pragmatics. Corporaare the main knowledge base in corpus linguistics and can be consideredas a type of foreign language writing aid as the contextualizedgrammatical knowledge acquired by non-native language users throughexposure to authentic texts in corpora can allow learners to grasp themanner of sentence formation in the target language, enabling effectivewriting.

One or more embodiments described herein include systems,computer-implemented methods, apparatus, and computer program productsthat facilitate outside-in term mappings for taxonomies and content.Frequently-occurring technical terms can be identified, which are commonacross multiple corpora. According to one embodiment, to identify knownequivalent terms, technical terms can be determined based on acomparison to a non-technical background corpus. In some embodiments,terms that have more than the threshold number of occurrences, orfrequency, in both corpora can be identified. For one or more terms t,which satisfies the above condition, a known equivalent pair can begenerated as (t,t).

FIG. 1 illustrates a block diagram of an example, non-limiting system100 that facilitates joint embedding of corpus pairs for outside-inmapping in accordance with one or more embodiments described herein. Invarious embodiments, the system 100 can be associated with or includedin a data analytics system, a data processing system, a graph analyticssystem, a graph processing system, a big data system, a social networksystem, a speech recognition system, an image recognition system, agraphical modeling system, a bioinformatics system, a data compressionsystem, an artificial intelligence system, an authentication system, asyntactic pattern recognition system, a medical system, a healthmonitoring system, a network system, a computer network system, acommunication system, a router system, a server system or the like.

In one embodiment, the system 100 can perform joint embedding based onreceipt of a first domain corpus and a second domain corpus. Forexample, in one embodiment, the system 100 can perform a joint embeddingapproach comprising embedding one or more domains comprising corporainto an embedding space using a statistical language model. In someembodiments, the domains can be asymmetric, meaning that one domain canhave significantly more data than another domain. The statisticallanguage model can be represented as a probability distribution oversequences of words in some embodiments. For example, given a sequence ofwords of length m, a probability P(w₁, . . . , w_(m)) can be assigned,wherein w₁ can represent a first word of a sequence of words. Theability for system 100 to estimate the relative likelihood of differentcombinations of word orders (e.g., different phrases) can be employed inmany natural language processing applications including, but not limitedto, speech recognition, machine translation, part of speech (POS)tagging, parsing, handwriting recognition, information retrieval andother applications.

Language models can also be used by system 100 in information retrievalin a query likelihood model. A separate language model can be associatedwith one or more documents in a collection. Documents can be rankedbased on the probability of the query in the document's language model.

According to system 100, in one or more embodiments, neural networklanguage models can be constructed and trained as probabilisticclassifiers that learn to predict a probability distribution, meaningthat the system 100 can be trained to predict a probability distributionover the vocabulary, given some linguistic context. In one embodiment,this can be done using standard neural network training algorithms suchas stochastic gradient descent with back propagation. The context can bea fixed-size window of previous words, so that the network predicts froma feature vector representing the previous k words. In anotherembodiment, system 100 can cause the neural network to learn thecontext, and given a word maximize the log-probability Σ_(−k≤j-1,j≤k)log P(w_(t+j)|w_(t)).

The system 100 can be employed to use hardware and/or software to solveproblems that are highly technical in nature (e.g., word searches,semantic tagging, determination of term frequency, matching of disparateterms within corpora composed of millions of terms), that are notabstract and that cannot be performed as a set of mental acts by a humandue to the processing capabilities need to facilitate unsupervised termmapping, for example. Further, some of the processes performed may beperformed by a specialized computer for carrying out defined tasksrelated to memory operations. For example, a specialized computer can beemployed to carry out tasks related to joint learning, content mappingor the like.

The systems 100, 200, 300, 400, 500, 600 and/or 700 and/or components ofthe systems 100, 200, 300, 400, 500, 600 and/or 700 can be employed tosolve new problems that arise through advancements in technology,computer networks, the Internet and the like. For example, the newproblems solved can be or include distribution and/or selection ofinformation for particular entities based on a relationship betweenterminology used on a user profile and terminology used on a pluralityof other user profiles.

In the embodiment shown in FIG. 1, the system 100 can include a jointembedding component 102, and content mapping component 104, which can beelectrically and/or communicatively coupled to one another in variousembodiments. As shown in FIG. 1, the joint embedding component 102 canbe communicatively coupled to the content mapping component 104. In anaspect, the content mapping component 104 can be or include a hardwareaccelerator for the processor that provides improved processingperformance. For example, processing performance and/or processingefficiency of the system 100 can be improved by employing one or more ofthe embodiments described herein in connection with the joint embeddingcomponent 102 and the content mapping component 104.

Joint embedding component 102 can be or include a processor that canperform joint embedding of multiple corpora. In one embodiment, jointembedding can be learned by the system 100 via a mathematical transformfrom one embedded domain to another. In another aspect, the jointembedding component 102 can be built based on a first domain corpus anda second domain corpus to form a corpus pair. Selected terms of thecorpus pair can then be considered equivalent terms to learn jointembedding. Known non-equivalent terms can be generated by randomlypermuting one of the two terms in a known equivalent pair.

In some embodiments, domain data related to the first domain corpus andthe second domain corpus can be received as an input by the system 100and, in some embodiments, as an input to the joint embedding component102. Accordingly, the joint embedding component 102 can jointly embedthe corpus pair.

In various embodiments, the first domain corpus and the second domaincorpus can be received from different locations. For instance, in someembodiments, the first domain corpus can be received from an externalwebsite of a company or entity while the second domain corpus can bereceived from an internal website to the company or entity.

Based on the first domain corpus input and the second domain corpusinput to the joint embedding component 102, a joint embedding model canbe built by the joint embedding component 102. In some embodiments, thefirst domain corpus input and/or the second domain corpus input cancomprise term data. The joint embedding model can build the jointembedding model to comprise term data from one or more (or, in someembodiments, both) of the first domain corpus and the second domaincorpus.

The content mapping component 104 can be a processor that can performmapping of terms associated with the first domain corpus to termsassociated with the second domain corpus. In one embodiment one or morewords can be mapped onto an n-dimensional real vector called the wordembedding, wherein n can be the size of the layer just before the outputlayer. Also, corpora from various different domains can be mapped to ajoint embedding. In some embodiments, the joint embedding can be used tomap a new term appearing in one domain to another term in the otherdomain by system 100. Unsupervised learning can stem from an associationof frequent technical terms, which are common across both corpora. Insome embodiments, a process can use a neural network in an unsupervisedmanner to map concepts from one domain (e.g., taxonomy) to another(e.g., internal corpus of a company).

In some embodiments, one or more input terms can be received by thecontent mapping component 104 from the input terms input into the jointembedding component 102. It should be noted that for the sake of brevity“data” can comprise “input terms” as shown in FIG. 1. In variousembodiments, the data can comprise some form of keywords, taxonomy tags,content, and/or user profiles. It should be appreciated that to performthe content mapping, the content mapping component 104 can receive anoutput of the joint embedding component 102 and an input (e.g., shown as“input terms” in FIG. 1) based on the keywords, taxonomy tags, content,and/or user profiles. In one embodiment, the mapping can be performed bymapping at least substantially equivalent terms between the first domaincorpus and the second domain corpus. After mapping the aforementionedinputs, the content mapping component 104 can generate output terms thatare equivalent between the first domain corpus and the second domaincorpus. The output terms can comprise internal taxonomy terms, genericterms, and/or generic content terms.

FIG. 2 illustrates another block diagram of an example, non-limitingsystem that facilitates joint embedding of corpus pairs for outside-inmapping in accordance with one or more embodiments described herein. Invarious embodiments, the system 200 can be a multi-processor systemand/or a multi-memory system. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity.

In some embodiments, a first domain corpus can be received as an inputat first corpus language embedding block 202. First corpus languageembedding block 202 can analyze and learn the language embedding for thefirst domain corpus. By way of example, but not limitation, the firstcorpus language embedding block 202 can analyze and/or learn thelanguage embedding for the first domain corpus by learning wordembeddings for terms within the first domain corpus. In someembodiments, first corpus language embedding block 202 can outputinformation indicative of the language embedding of the first domaincorpus to block 208.

A second domain corpus can be received as an input to second corpuslanguage embedding block 204. Second corpus language embedding block 204can analyze and learn the language embedding for the second domaincorpus. By way of example, but not limitation, the second corpuslanguage embedding block 204 can analyze and/or learn the languageembedding for the second domain corpus by learning word embeddings forterms within the first domain corpus. In some embodiments, second corpuslanguage embedding block 204 can output information indicative of thelanguage embedding of the second domain corpus to block 208. It shouldbe appreciated that although there are only two domains representedwithin FIG. 2, the system 200 can process data from more than twodomains in a similar manner to that described for the two domaincorpora.

In some embodiments, as shown, both the first domain corpus embeddingand the second domain corpus embedding can be output from the firstcorpus language embedding block 202 and/or the second corpus languageembedding block 204 and received as inputs at first corpus and secondcorpus joint embedding learning (FSJEL) block 208. Furthermore,equivalent terms between the first domain corpus and the second domaincorpus can be identified by equivalent terms identification block 206.Equivalent terms identification block 206 can identify known equivalentterms between the first domain corpus and the second domain corpus byidentifying technical terms that are common to both corpora, wheretechnical terms can be identified by comparing to backgroundnon-technical corpus. In some embodiments, the equivalent termsidentification block 206 can output one or more known equivalent termsto FSJEL block 208.

In some embodiments, FSJEL block 208 can receive embedding data relatedto the learned language embedding from first corpus language embeddingblock 202, the learned language embedding from the second corpuslanguage embedding block 204, and equivalent term data related to theone or more known equivalent terms from equivalent terms identificationblock 206. The aforementioned inputs to FSJEL block 208 can be processedby FSJEL block 208 to learn a joint embedding between the first domaincorpus and the second domain corpus.

In some embodiments, the learned joint embedding as processed by FSJELblock 208 can generate a scoring and/or an affinity value as related tothe similarity of two terms across both domains. Thereafter, the learnedjoint embedding comprising an assessment of the mapping between thephrases of the two corpora can be output from FSJEL block 208 as a modelfor further processing. As shown, the model can be one or more modelmatrices in some embodiments. In some embodiments, the model trainingdata can be received by computation blocks 404, 406 (described belowwith reference to FIG. 4) to leverage across other domains to generateadditional terms.

FIG. 3 illustrates yet another block diagram of an example, non-limitingsystem that facilitates joint embedding of corpus pairs for outside-inmapping in accordance with one or more embodiments described herein.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

In the embodiment shown in FIG. 3, the system 300 can comprise aninternal corpus 302 and an external corpus 306. In some embodiments“internal corpus” can mean a repository of words internal to a companyor entity and “external corpus” can be a repository of words external tothe company or entity. In one aspect of FIG. 3, the internal corpus 302can be a database of terms specific to a company, entity, organizationand/or a marketing platform (e.g., internet webpage). Alternatively, insome embodiments, the external corpus 306 can be a database of termsthat are external to the internal corpus 302. Therefore, in an aspect,one output of the system 300 can be information that can indicate and/orbe employed to determine which terms between the internal corpus 302 andthe external corpus 306 are equivalent and/or substantially equivalent.

The internal corpus 302 can be received as an input at learning block304. Learning block 304 can then analyze and learn the languageembedding for the internal corpus 302 by assessing the internal corpus302. For example, the internal corpus 302 can be assessed by analyzingthe sequence of words and the context in which the words appear todetermine the language embedding via equation 1 below:

$\begin{matrix}{V^{1},{U^{1} = {{{argmax}_{V,U}{\prod\limits_{w^{1},c^{1}}\;{S\left( {UVw}^{1} \right)}}}❘c^{1}}}} & (1)\end{matrix}$where w¹ is a vector representing a word appearing in the corpus, c¹ isthe ordered set of words in a fixed window on either side of w¹, V¹ isan embedding matrix used to map w¹ the embedding space, U¹ a predictionmatrix that can predict c¹ from the embedding of w¹, and S is anon-linear scoring function that evaluates the goodness of fit betweenthe prediction and c¹. Equation 1 selects an embedding matrix V¹ thatproduces an embedding that when combined with U¹ can accurately predictthe context in which a word is likely to appear. Words with similarembeddings using the above model can have similar meanings as words withsimilar embeddings are predicted to be usable in the same context.

The external corpus 306 can be received as an input to learning block308. Learning block 308 can analyze and learn the language embedding forthe external corpus 306 by assessing the external corpus 306. Forexample, the external corpus 306 can be assessed for a frequency ofwords. For example, the external corpus 306 can be assessed for afrequency of words, wherein the frequency of words can determine thelanguage embedding, wherein the frequency of words can determine thelanguage embedding via equation 2 below:

$\begin{matrix}{V^{2},{U^{2} = {{{argmax}_{V,U}{\prod\limits_{w^{2},c^{2}}\;{S\left( {UVw}^{2} \right)}}}❘c^{2}}}} & (2)\end{matrix}$where w² is a vector representing a word appearing in the corpus, c² isthe ordered set of words in a fixed window on either side of w², V² isan embedding matrix used to map w² into the embedding space, U² is aprediction matrix that can predict c² from the embedding of w², and S isa non-linear scoring function that evaluates the goodness of fit betweenthe prediction and c². Equation 2 selects an embedding matrix V² thatproduces an embedding that when combined with U² can accurately predictthe context in which a word is likely to appear. Words with similarembeddings using the above model can have similar meanings as words withsimilar embeddings are predicted to be usable in the same context.

It should be appreciated that although there are only two corporarepresented within FIG. 3, the system 300 can be capable of processingdata from multiple corpora in a similar manner. As shown in FIG. 3, theprocessing component 310 can also include a joint embedding transformcomponent 312 and a communication component 316. In some embodiments,the joint embedding transform component 312 can include a learningcomponent 320. Aspects of the processing component 310 can constitutemachine-executable component(s) embodied within machine(s), e.g.,embodied in one or more computer readable mediums (or media) associatedwith one or more machines. Such component(s), when executed by the oneor more machines, e.g., computer(s), computing device(s), virtualmachine(s), etc. can cause the machine(s) to perform the operationsdescribed. In an aspect, the processing component 310 can also includememory 318 that stores computer executable components and instructions.Furthermore, the processing component 310 can include a processor 314 tofacilitate operation of the instructions (e.g., computer executablecomponents and instructions) by the processing component 310.

The joint embedding transform component 312 can receive input data(e.g., INPUT DATA shown in FIG. 3). In an aspect, the input data cancorrespond to the data described with reference to FIG. 1. For example,in various embodiments, the input data can be a portion (e.g., asection) of the data. The input data can be received, for example, via anetwork. Alternatively, in some embodiments, the input data can bereceived from a database in communication with the processing component310 via a network. In some embodiments, the input data can be data thatis transmitted to the processing component 310 and other processingcomponents. In one example, the input data can be a portion of trainingdata (e.g., a trained data set, one or more inputs, etc.) associatedwith a learning training and/or a mapping process.

The input data from both domains can be processed by the joint embeddingtransform component 312 via a joint embedding transform between the twodomains. For example, in one embodiment, a joint embedding transform Acan be learned by a stochastic gradient decent (SGD) by way of equation3:arg max_(A) f(w _(1,t) ^(T) V ¹ AV ² w _(2,t) ,w _(1,f) ^(T) V ¹ AV ² w_(2,f))  (3)where w_(1,t) and w_(2,t) are input matrixes where the correspondingrows of these two matrixes are the representation of pairs of equivalentterms that were identified between the two corpora, w_(1,f) and w_(2f)are input matrixes where the corresponding rows of these two matrixesare the representation of pairs of non-equivalent terms that wereidentified in the corpora, and V¹ and V² are the embedding vectorslearned in Equation 1 and 2 above for the first and second corporarespectively, and A is a transformation matrix that is learned totransform between the embedding spaces of each corpora.

In one embodiment of equation 3, w_(1,t)˜w_(2,t) can be known equivalentterms, w_(1,f)˜w_(2,f) are know non-equivalent terms, and f can be usedto maximize the first term or maximize a margin between the two terms.It should also be noted that V¹, U¹, V², U², and A can be learnedjointly. Based on the input data, the joint embedding transformcomponent 312 can generate output data (e.g., OUTPUT DATA shown in FIG.3). The output data can be generated, for example, in response to atraining/mapping processing (e.g. a mapping process associated with theinput data) that is performed by the joint embedding transform component312. In one example, the output data can be associated with a model fora neural network associated with processing components. In an aspect,the output data can be stored in the memory 318 or another memoryassociated with the processing component 310.

In an embodiment, as shown, the joint embedding transform component 312can include a learning component 320. Alternatively, the learningcomponent 320 can be external to the joint embedding transform component312. The learning component 320 can perform one or more machine learningcomputations associated with the data. For example, the learningcomponent 320 can perform one or more clustering machine learningcomputations, one or more decision tree machine learning computations,one or more instance-based machine learning computations, one or moreregression machine learning computations, one or more regularizationmachine learning computations, one or more rule learning machinelearning computations, one or more Bayesian machine learningcomputations and/or one or more different machine learning computations.In one example, the learning component 320 can perform one or more modellearning computations associated with the data. For example, thelearning component 320 can perform one or more Bayesian computations,one or more network computations, and/or one or more convolution neuralnetwork computations.

FIG. 4 illustrates an additional block diagram of an example,non-limiting system that facilitates joint embedding of corpus pairs foroutside-in mapping in accordance with one or more embodiments describedherein. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

In various embodiments, the system 400 can be perform outside-inmapping. In some embodiments, outside-in mapping can include receiving afirst domain input, a second domain input, and inputs based on a trainedmodel. In some embodiments, the trained model can be associated with afirst domain corpus and a second domain corpus. In some embodiments oneor more components of system 400 can be included in the content mappingcomponent 104. It should also be noted that FIG. 4 is another embodimentof components associated with a method of performing the processesassociated with FIG. 3. Therefore, in some embodiments, FIG. 3 can beemployed and in some embodiments FIG. 4 can be employed. Moreover, thesystem 400 can be associated with or be included in a data analyticssystem, a data processing system, a graph analytics system, a graphprocessing system, a big data system, a social network system, a speechrecognition system, an image recognition system, a graphical modelingsystem, a bioinformatics system, a data compression system, anartificial intelligence system, an authentication system, a syntacticpattern recognition system, a medical system, a health monitoringsystem, a network system, a computer network system, a communicationsystem, a router system, a server system, a high availability serversystem (e.g., a Telecom server system), a Web server system, a fileserver system, a data server system, a disk array system, a poweredinsertion board system, a cloud-based system or the like.

In the embodiment shown in FIG. 4, the system 400 can comprise anextraction block 402, computation blocks 404, 406, a scoring block 408,and/or an aggregation block 410, one or more of which can beelectrically and/or communicatively coupled to one another. As shown inFIG. 4, a first domain term collection, which can be indicated as “inputterms” of FIG. 4, can be input into system 400. The term collection cancomprise keywords, web-page content, and/or user-profile content. Of theterm collection input, a specific term t can be extracted from the termcollection at extraction block 402. It should be noted that in thisscenario, the first domain term collection is based on an externalcorpus whereas the second domain terms are related to an internalcorpus. The trained model input into computation blocks 404, 406 can bethe trained model output from FIGS. 2 and 3. Consequently, based on thetrained model information, computation block 404 can compute an embeddedrepresentation of term t using the trained model and computation block406 can compute an embedded representation for one or more of the seconddomain terms as represented by the internal corpus. It should be notedthat the computations of computation blocks 404 and 406 can be performedsimultaneously, concurrently or linearly, meaning that computation block404 does not have to compute its embedded representation before thecomputation block 406 computes its embedded presentation, although thispermutation is possible. It should also be noted that, in someembodiments, one or more of computation block 404 processes andcomputation block 406 processes can be performed by one component.

The system 400 can provide a score for a joint embedding affinity withterm t for one or more of the second domain terms at scoring block 408.The trained model can also be received as an input to scoring block 408to generate a joint embedding affinity. In one embodiment, the scoringblock 408 can return ranked lists of x terms in descending or ascendingorder of joint score. In some embodiments, external inputs, and rankedentities, can be term collections instead of terms (e.g., specific pieceof content or user-content profiles).

In some embodiments, affinity scores can be computed and aggregatedacross input and output term collections. For example, a Bayesiangeneration model can be used, where one or more input terms generate anoutput term with a probability as a function of the affinity score. Anoverall score can be generated based on a function of a likelihood of anoutput given the input P(x₁x₂x₃ . . . |t₁t₂t₃ . . . ) where t₁t₂t₃ . . .can be a collection of terms from the external domain 1, and x₁x₂x₃ . .. can be a collection from domain 2.

Equivalent terms in the two corpora can be identified by inferring thattextually similar terms are equivalent. It should be noted that knownnon-equivalent terms can also be generated by randomly permuting one ofthe two terms in a known equivalent pair. Terms can be furtheridentified by finding terms that have more than a threshold number ofoccurrences or frequency in both corpora. For instance, the externaldomain can be profile page resume information regarding a person'sexperience with cognitive computing. Therefore, “cognitive computing”can be extracted as term t and compared against many similar termswithin the internal corpus such as “artificial intelligence.”Consequently, scoring block 408 can determine a frequency with which thewords “cognitive computing” are used through the external corpus and theinternal corpus and assign a score to the terms “cognitive computing”and “artificial intelligence” accordingly. The joint embedding affinityscore can then be aggregated across the first domain and the seconddomain term collections.

Furthermore, in some embodiments, the term collection rankings can beoutput and can comprise, but are not limited to, keywords, taxonomytags, content, and/or user profiles. It should be noted that thecomparison between the first domain corpus and the second domain corpuscan be in the form of comparing one document to many documents,respectively. It should also be noted that the system can be used forcontent-personalization purposes, for example, for use in a digitalmarketing platform. For instance, the first domain corpus can be a userprofile, and the second domain corpora can be webpages of a website.

The comparison between the first domain corpus and the website canprovide an affinity value for terms that are frequent across the userprofile and the website. In some embodiments the affinity values for aplurality of terms from each webpage of the website can be aggregated tocompute an affinity score for each webpage. The webpages with a highaffinity value (e.g., an affinity value greater than or equal to adefined affinity threshold) can then be selected for display to a userassociated with the user profile via a display screen. Likewise, thecomparison between the first domain corpus and the one or more otheruser profiles associated with the second domain corpora can provide anaffinity value for terms that are frequent across the user profile andthe one or more other user profiles. In some embodiments the affinityvalues for a plurality of terms from each of the user-profiles of thesecond corpus, can be aggregated to compute an aggregated affinity scorefor each user profile from the second corpus. The user-profiles from thesecond corpus with a high aggregated affinity value can then be used toidentify content data that may be of interest to the user with the userprofile from the first corpus. In some embodiments, this can be doneusing a recommender system.

In one embodiment, a set of named entities is identified in each corporausing text processing techniques where textually similar named entitiescan be identified as equivalent.

FIG. 5 illustrates an embodiment of a joint embedding component inaccordance with one or more embodiments described herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

In the embodiment shown, the joint embedding component 102 comprisesseveral components including, but not limited to, a learning component502, an identification component 504, and a joint learning component506. It should be noted that the learning component 502, theidentification component 504, and the joint learning component 506 canbe electrically and/or communicatively coupled within the jointembedding component 102 in some embodiments. The learning component 502can receive domain corpora data from several different domains. Thedomain corpora data can comprise profile data, content data, keyworddata, etc. The learning component 502 can learn outside-in term mappingsbetween the corpora for taxonomies and content in an automatedunsupervised manner. Therefore, in some embodiments, one or more of thedomain corpora can be embedded into the learning component 502 using aneural network language-model.

In some embodiments, the identification component 504, can identifyknown equivalent terms between the domain corpora. It should be notedthat the identification component 504 can also identify non-equivalentterms between the domain corpora. Term identification can be facilitatedby identifying technical terms across both corpora. Alternatively, termidentification can also be accomplished by comparing a non-technicalbackground corpus to the terms of another corpus. A threshold value canalso be assigned to the terms to help characterize whether the frequencyof the terms within the corpora is high frequency.

The joint learning component 506 can leverage a mathematical transformfrom one embedded domain to another. For instance, the joint learningcomponent 506 can leverage a SGD to learn a joint embedding transform. Astochastic gradient decent is a stochastic approximation of the gradientdescent optimization method for minimizing an objective function that iswritten as a sum of differentiable functions. Thus, in some embodiments,SGD can determine one or more minimums or maximums by iteration. Byapplying the SGD to the terms across various domain corpora, the system500 can determine a representation of one or more terms and generate anaffinity score based on the similarity of term representations. Thejoint learning component 506 can also leverage an unsupervised learningmethodology comprising inferring a function to describe a hiddenstructure from unlabeled data. Since the examples given are unlabeled,there is no error or reward signal to evaluate a potential solution. Invarious embodiments, unsupervised learning can encompass one or moretechniques that seek to summarize and explain key features of the dataincluding, but not limited to, k-means, mixture models, hierarchicalclustering, anomaly detection, neural networks, etc.

FIG. 6 illustrates an embodiment of a content mapping component inaccordance with one or more embodiments described herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The content mapping component 104 as represented in FIG. 6 can include,but is not limited to, an extraction component 602, a computationcomponent 604, and/or a scoring component 606, one or more of which canbe electrically and/or communicatively coupled to one another in variousembodiments. It should be noted that the extraction component 602, thecomputation component 604, and the scoring component 606 can becommunicatively coupled within the content mapping component 104.

In some embodiments, the extraction component 602 can extract specificterms related to keywords, content, and/or a user profile associatedwith a first domain corpus. The computation component 604 can receivetrained model data from the joint embedding component 102 as representedby FIG. 5. The trained model data can be used to compute embeddedrepresentations of an extracted term from the extraction component 602and embedded representations of terms associated with a second domaincorpus.

The scoring component 606 can provide a score for a joint embeddingaffinity with the extracted term against the second domain corpus. Thescore can be based on the similarity between the representation of theextracted term (possibly processed through a learned transform such as alinear transform) and the representation of one or more terms in thesecond domain corpus.

FIG. 7 illustrates yet another example, non-limiting system thatfacilitates joint embedding of corpus pairs for outside-in mapping inaccordance with one or more embodiments described herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The system 700 of FIG. 7 represents another embodiment of the jointembedding component 102 and the content mapping component 104 beingelectrically and/or communicatively coupled to form the system 700. Thejoint embedding component 102 can comprise several components including,but not limited to, a learning component 502, an identificationcomponent 504, and a joint learning component 506. It should be notedthat the learning component 502, the identification component 504, andthe joint learning component 506 can be communicatively coupled withinthe joint embedding system. The learning component 502 can receivedomain corpora data from several different domains. The domain corporadata can comprise profile data, content data, keyword data, etc. Thelearning component can learn outside-in term mappings between thecorpora for taxonomies and content in an automated unsupervised manner.Therefore, one or more domain corpora can be embedded into the learningcomponent 704 using a neural network language-model.

The identification component 504 can identify known substantiallyequivalent terms between the domain corpora. It should be noted that theidentification component 504 can also identify non-equivalent termsbetween the domain corpora. Term identification can be facilitated byidentifying technical terms across both corpora. Alternatively, termidentification can also be accomplished by comparing a non-technicalbackground corpus to the terms of another corpus. A threshold value canalso be assigned to the terms to help determine the frequency of theterms within the corpora.

The joint learning component 506 can leverage a mathematical transformfrom one embedded domain to another. For instance, the joint learningcomponent 506 can leverage SGD to learn a joint embedding transform. Thejoint learning component 506 can also leverage an unsupervised learningmethodology comprising inferring a function to describe a hiddenstructure from unlabeled data.

The content mapping component 104 as represented in FIG. 7 can include,but is not limited to, an extraction component 602, a computationcomponent 604, and a scoring component 606. It should be noted that theextraction component 602, the computation component 604, and the scoringcomponent 606 can be electrically and/or communicatively coupled withinthe content mapping component 104. The extraction component 602 canextract specific terms related keywords, content, and/or a user profileassociated with a first domain corpus. The computation component 604 canreceive trained model data from the joint embedding component 102. Thetrained model data can be used to compute embedded representations of anextracted term from the extraction component 602 and embeddedrepresentations of terms associated with a second domain corpus.

The scoring component 606 can provide a score for a joint embeddingaffinity with the extracted term against the second domain corpus. Thescore can be based on the similarity between the representation of theextracted term (possibly processed through a learned transform such as alinear transform) and the representation of one or more terms in thesecond domain corpus.

FIG. 8 illustrates a flow diagram of an example, non-limitingcomputer-implemented method 800 that facilitates a mapping process inaccordance with one or more embodiments described herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

The computer-implemented method 800 can be performed by and/orassociated with a plurality of processing components. At 802, one ormore inputs associated with a mapping process can be received by theplurality of processing components (e.g., by processing component 310).For example, one or more of the processing components of the pluralityof processing components can receive a different one or more inputsassociated with the learning process (e.g., by the joint embeddingcomponent 102). The one or more inputs can be associated with trainingdata for a mapping process. For instance, the one or more inputs can bea plurality of first keywords associated with a first domain corpus. At804, the processing components can extract a first keyword of theplurality of first keywords (e.g., by the extraction component 602). Thefirst keyword can be a keyword associated with a profile and/or webpagecontent of a first domain corpus. At 806, a plurality of second keywordsassociated with a second domain corpus can be received as one or moreinputs to the processing components (e.g., by the joint embeddingcomponent 102). The second keywords can be associated with one or moretaxonomy tags and/or one or more keyword sets and/or with one or moreuser profiles and/or webpage content from the second domain corpus. Datagenerated by a processing component, of the processing components, toform a trained model is provided to all other processing components in agroup of processing components that can also include the processingcomponent and the other processing components. At 808, based on theinputs, a first embedded representation of the first keyword via thetrained model is generated and a second embedded representation of thesecond keywords via the trained model is generated by the processingcomponents (e.g., by the joint embedding component 102).

The processing components can also score a joint embedding affinityassociated with a joint embedding at 810 (e.g., by the scoring component606), wherein the scoring the joint embedding affinity can compriseseveral actions. At 812, an action can comprise transforming the firstembedded representation of the first keyword and the second embeddedrepresentation of the second keywords via the trained model. At 814,another action can comprise determining an affinity value based oncomparing the first keyword to the second keywords. And yet anotheraction can comprise, based on the affinity value, aggregating the jointembedding of the first embedded representation of the first keyword andthe second embedded representation of the second keywords within thesecond domain corpus at 816. Furthermore, a group to which theprocessing component belongs can be repeatedly changed during thelearning process. For example, after one or more processing acts duringthe learning process, the processing component can be assigned to a newgroup of processing components for exchanging data. As such, theplurality of processing components can independently perform thelearning process to facilitate local amalgamation of data andcomputation of composite output associated with the plurality ofprocessing components.

FIG. 9 illustrates a flow diagram of another example, non-limitingcomputer-implemented method 900 that facilitates development of atraining model in accordance with one or more embodiments describedherein. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

At 902, first domain data associated with a domain comprising a firstcorpus can be analyzed, resulting in first analyzed data (e.g., by thejoint embedding component 102). For example, the first domain data canbe received via a network. In an aspect, the first domain data can bereceived from or stored in a database. The first domain data cancomprise first terms associated with a user identity profile of a firstdomain corpus. In various embodiments, the first domain data can alsocomprise webpage data, webpage history data, or data found within a textdocument. In another aspect, other portions of the first domain data canbe transmitted to other processing components. At 904, second domaindata associated with a second domain comprising a second corpus can beanalyzed, resulting in second analyzed data (e.g., by the jointembedding component 102). In various embodiments, the second domain datacan comprise taxonomy data, webpage data, user-profile data, keyword setdata, and/or data found within a text document. At 904, the seconddomain data can be processed (e.g., by a computation component 604 ofthe content mapping component 104) to generate output data (e.g., forassociated domains) in the form of a trained model.

At 906, based on the analyzing the first domain data and the analyzingthe second domain data, equivalent terms between the first domain dataand the second domain data can be identified (e.g., by the jointembedding component 102). At 908, based on the equivalent terms, thefirst analyzed data, and the second analyzed data, the first domain dataand the second domain data can be jointly embedded, resulting in jointlyembedded data (e.g., by the joint embedding component 102). Furthermore,in response to the joint embedding, a trained model associated with thejointly embedded data can be output at 910.

The output data can comprise a first embedded representation of aprofile term of the first terms, associated with the user identityprofile, via a trained model; and a second embedded representation ofsecond terms, associated with a second domain corpus, via the trainedmodel. An indication of a group of processing components can be received(e.g., by a computation component 604 of the content mapping component104). For example, the indication of the group of processing componentscan be received via a network. Additionally, the indication of the groupcan be received from a joint learning component 506. Another processingcomponent can then receive the output data at from the output at 910 andcompare the profile term to the second terms to determine an affinityvalue based on the joint embedding, resulting in a comparison (e.g., bythe scoring component 606). Based on the affinity value, the otherprocessing component can aggregate the joint embedding of the firstembedded representation of the profile term and the second embeddedrepresentation of the second terms within the first domain corpus andthe second domain corpus.

The output data can be transmitted (e.g., by the scoring component 606)in the form of the affinity value and the profile term to the seconddomain corpus. It should be noted that the output data can be sent toone or more of the domains from which corpora was received as an inputto the group of processing components. Data from one or more of theprocessing components in the group of processing components can bereceived (e.g., by the joint learning component 506 of the jointembedding component 102). For example, one or more parameters from oneor more of the processing components in the group of processingcomponents can be received by the joint embedding system. In anotherexample, one or more weights from one or more of the processingcomponents in the group of processing components can be received. In yetanother example, one or more gradients from one or more of theprocessing components in the group of processing components can bereceived. For example, the output data generated by further processingthe input data based on the data can be combined with other dataassociated with other processing components involved in the training andmapping processes. As such, composite output data associated with thegroup of processing components can be generated. Additionally oralternatively, the output data generated by further processing the inputdata based on the data can be stored in a memory and/or transmitted to aremote device (e.g., a server device) via a network.

FIG. 10 illustrates a flow diagram of another example, non-limitingcomputer-implemented method 1000 that facilitates display of data basedon an affinity value associated with a user profile in accordance withone or more embodiments described herein. Repetitive description of likeelements employed in other embodiments described herein is omitted forsake of brevity.

At 1002, a first embedded representation of a profile term of the firstterms, associated with a user identity profile can be generated via atrained model, wherein the first terms are associated with the useridentity profile of a first domain corpus (e.g., by the learningcomponent 502 of the joint embedding component 102 of system 700). Thefirst domain corpus can be embedded by a learning component based oninput data associated with training data to be generated by theprocessing components based on the input data (e.g., by the learningcomponent 502 of the joint embedding component 102). The embedded datais analyzed, by the learning component based on a condition associatedwith the embedded data being determined to have been satisfied, whereinthe analyzing can comprise an unsupervised estimation of a statisticaldistribution characterizing the embedded data (e.g., stored by thememory 318). At 1004, a second embedded representation of second terms,associated a second domain corpus, can be generated via the trainedmodel (e.g., by the learning component 502 of the joint embeddingcomponent 102 of system 700).

At 1006, the profile term can be compared to the second terms todetermine an affinity value based on a joint embedding of the firstembedded representation and the second embedded representation,resulting in a comparison data (e.g., by the scoring component 606 ofthe content mapping component 104 of system 700). Based on thecomparing, the profile term can be mapped to the second terms (e.g., bythe content mapping component 104). Mapping the profile term to thesecond terms can result in mapped data, wherein the mapped data can beoutput to various user identity profiles so that a scoring valueassociated with specific terms can be aggregated across the various userprofiles. In various different embodiments, the scoring value cancomprise a term collection ranking, keyword, taxonomy tag, content,and/or user profile data. The terms can also be ranked in descending orascending order based on their associated scoring value.

At 1008, display data associated with the comparison data can begenerating based on the affinity value and for display by a webpage(e.g., by the system 700). Because the display is based on an affinityvalue associated with the user profile to a web page, another userprofile, or a plurality of user profiles, the display can berepresentative of commonalities between the user profile and the webpage, other user profile, and/or the plurality of user profiles.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 11 as well as the following discussion is intendedto provide a general description of a suitable environment in which thevarious aspects of the disclosed subject matter can be implemented. FIG.11 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated. With reference to FIG. 11, a suitable operating environment1100 for implementing various aspects of this disclosure can alsoinclude a computer 1112. The computer 1112 can also include a processingunit 1114, a system memory 1116, and a system bus 1118. The system bus1118 couples system components including, but not limited to, the systemmemory 1116 to the processing unit 1114. The processing unit 1114 can beany of various available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit1114. The system bus 1118 can be any of several types of busstructure(s) including the memory bus or memory controller, a peripheralbus or external bus, and/or a local bus using any variety of availablebus architectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI).

The system memory 1116 can also include volatile memory 1120 andnonvolatile memory 1122. The basic input/output system (BIOS),containing the basic routines to transfer information between elementswithin the computer 1112, such as during start-up, is stored innonvolatile memory 1122. By way of illustration, and not limitation,nonvolatile memory 1122 can include read only memory (ROM), programmableROM (PROM), electrically programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), flash memory, or nonvolatile random accessmemory (RAM) (e.g., ferroelectric RAM (FeRAM). Volatile memory 1120 canalso include random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronousDRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM(ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM), directRambus dynamic RAM (DRDRAM), and Rambus dynamic RAM.

Computer 1112 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 11 illustrates, forexample, a disk storage 1124. Disk storage 1124 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 1124 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 1124 to the system bus 1118, a removableor non-removable interface is typically used, such as interface 1126.FIG. 11 also depicts software that acts as an intermediary between usersand the basic computer resources described in the suitable operatingenvironment 1100. Such software can also include, for example, anoperating system 1128. Operating system 1128, which can be stored ondisk storage 1124, acts to control and allocate resources of thecomputer 1112.

System applications 1130 take advantage of the management of resourcesby operating system 1128 through program modules 1132 and program data1134, e.g., stored either in system memory 1116 or on disk storage 1124.It is to be appreciated that this disclosure can be implemented withvarious operating systems or combinations of operating systems. A userenters commands or information into the computer 1112 through inputdevice(s) 1136. Input devices 1136 include, but are not limited to, apointing device such as a mouse, trackball, stylus, touch pad, keyboard,microphone, joystick, game pad, satellite dish, scanner, TV tuner card,digital camera, digital video camera, web camera, and the like. Theseand other input devices connect to the processing unit 1114 through thesystem bus 1118 via interface port(s) 1138. Interface port(s) 1138include, for example, a serial port, a parallel port, a game port, and auniversal serial bus (USB). Output device(s) 1140 use some of the sametype of ports as input device(s) 1136. Thus, for example, a USB port canbe used to provide input to computer 1112, and to output informationfrom computer 1112 to an output device 1140. Output adapter 1142 isprovided to illustrate that there are some output devices 1140 likemonitors, speakers, and printers, among other output devices 1140, whichrequire special adapters. The output adapters 1142 include, by way ofillustration and not limitation, video and sound cards that provide ameans of connection between the output device 1140 and the system bus1118. It should be noted that other devices and/or systems of devicesprovide both input and output capabilities such as remote computer(s)1144.

Computer 1112 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1144. The remote computer(s) 1144 can be a computer, a server, a router,a network PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 1112.For purposes of brevity, only a memory storage device 1146 isillustrated with remote computer(s) 1144. Remote computer(s) 1144 islogically connected to computer 1112 through a network interface 1148and then physically connected via communication connection 1150. Networkinterface 1148 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), cellularnetworks, etc. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL). Communicationconnection(s) 1150 refers to the hardware/software employed to connectthe network interface 1148 to the system bus 1118. While communicationconnection 1150 is shown for illustrative clarity inside computer 1112,it can also be external to computer 1112. The hardware/software forconnection to the network interface 1148 can also include, for exemplarypurposes only, internal and external technologies such as, modemsincluding regular telephone grade modems, cable modems and DSL modems,ISDN adapters, and Ethernet cards.

The present disclosure may be a system, a method, an apparatus and/or acomputer program product at any possible technical detail level ofintegration. The computer program product can include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure. The computer readable storage medium can be atangible device that can retain and store instructions for use by aninstruction execution device. The computer readable storage medium canbe, for example, but is not limited to, an electronic storage device, amagnetic storage device, an optical storage device, an electromagneticstorage device, a semiconductor storage device, or any suitablecombination of the foregoing. A non-exhaustive list of more specificexamples of the computer readable storage medium can also include thefollowing: a portable computer diskette, a hard disk, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), a portable compact disc read-only memory (CD-ROM), a digitalversatile disk (DVD), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, and any suitable combination ofthe foregoing. A computer readable storage medium, as used herein, isnot to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of the present disclosure canbe assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions can execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer can beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection can be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) can execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments in which tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable components comprise: an extraction component that:extracts a first keyword of a plurality of first keywords associatedwith a first corpus; a computation component that: generates a firstembedded representation of the first keyword via a trained model andgenerates a second embedded representation of second keywords via thetrained model, wherein the second keywords are associated with a secondcorpus; and a scoring component that: scores a joint embedding affinityassociated with a joint embedding of the first embedded representationof the first keyword and the second embedded representation of thesecond keywords within the second domain corpus, resulting in a scoringof the joint embedding affinity, wherein the scoring of the jointembedding affinity comprises: transforming the first embeddedrepresentation of the first keyword and the second embeddedrepresentation of the second keywords via the trained model; determiningan affinity value based on comparing the first keyword to the secondkeywords; and based on the affinity value, aggregating the jointembedding of the first embedded representation of the first keyword andthe second embedded representation of the second keywords within thesecond domain corpus.
 2. The system of claim 1, wherein the plurality offirst keywords comprises a second keyword associated with a useridentity associated with a user profile and the second keywords comprisea plurality of user identities associated with a plurality of userprofiles.
 3. The system of claim 1, wherein the affinity value isgenerated in response to an unsupervised estimation, and wherein theunsupervised estimation comprises a cluster analysis of the plurality offirst keywords and the second keywords.
 4. The system of claim 1,wherein the plurality of first keywords comprises a second keywordassociated with a plurality of web pages.
 5. The system of claim 1,wherein the affinity value is related to a scoring of the jointembedding of the first embedded representation and the second embeddedrepresentation.
 6. The system of claim 1, wherein the scoring the jointembedding affinity comprises, based on satisfaction of a definedcondition associated with a frequency of the first keyword, outputting akeyword collection ranking, resulting in a ranked first keyword, to thesecond domain corpus.
 7. A computer program product for managing amapping process, the computer program product comprising a computerreadable storage medium having program instructions embodied therewith,the program instructions executable to: input a plurality of firstkeywords associated with a first domain corpus; extract a first keywordof the plurality of first keywords; input a plurality of second keywordsassociated with a second domain corpus; generate a first embeddedrepresentation of the first keyword via a trained model and generate asecond embedded representation of the second keywords via the trainedmodel; and score a joint embedding affinity associated with a jointembedding, wherein the scoring the joint embedding affinity comprises:transforming the first embedded representation of the first keyword andthe second embedded representation of the second keywords via thetrained model; determining an affinity value based on comparing thefirst keyword to the second keywords; and based on the affinity value,aggregating the joint embedding of the first embedded representation ofthe first keyword and the second embedded representation of the secondkeywords within the second domain corpus.
 8. The computer programproduct of claim 7, wherein the affinity value is related to a scoringof the joint embedding of the first embedded representation and thesecond embedded representation.
 9. The computer program product of claim8, wherein the program instructions are further executable to: based onsatisfaction of a defined condition associated with a frequency of thefirst keyword, output a keyword collection ranking, resulting in aranked first keyword, to the second domain corpus.
 10. The computerprogram product of claim 9, wherein the keyword collection ranking isranked in a descending order.
 11. The computer program product of claim9, wherein the keyword collection ranking comprises taxonomy tag dataassociated with a taxonomy tag.
 12. The computer program product ofclaim 9, wherein the program instructions are further executable to:output the keyword collection ranking to the first domain corpus. 13.The computer program product of claim 7, wherein the plurality of firstkeywords comprises a second keyword associated with a user identityassociated with a user profile and the plurality of second keywordscomprises a plurality of user identities associated with a plurality ofuser profiles.
 14. The computer program product of claim 7, wherein theaffinity value is generated in response to an unsupervised estimation,and wherein the unsupervised estimation comprises a cluster analysis ofthe plurality of first keywords and the plurality of second keywords.15. The computer program product of claim 7, wherein the plurality offirst keywords comprises a second keyword associated with a plurality ofweb pages.
 16. A computer-implemented method, comprising: inputting, bya device operatively coupled to a processor, a plurality of firstkeywords associated with a first domain corpus; extracting, by thedevice, a first keyword of the plurality of first keywords; inputting,by the device, a plurality of second keywords associated with a seconddomain corpus; generating, by the device, a first embeddedrepresentation of the first keyword via a trained model and generating asecond embedded representation of the second keywords via the trainedmodel; and scoring, by the device, a joint embedding affinity associatedwith a joint embedding, wherein the scoring the joint embedding affinitycomprises: transforming the first embedded representation of the firstkeyword and the second embedded representation of the second keywordsvia the trained model; determining an affinity value based on comparingthe first keyword to the second keywords; and based on the affinityvalue, aggregating the joint embedding of the first embeddedrepresentation of the first keyword and the second embeddedrepresentation of the second keywords within the second domain corpus.17. The computer-implemented method of claim 16, wherein the affinityvalue is related to a scoring of the joint embedding of the firstembedded representation and the second embedded representation.
 18. Thecomputer-implemented method of claim 17, further comprising: based onsatisfaction of a defined condition associated with a frequency of thefirst keyword, outputting, by the device, a keyword collection ranking,resulting in a ranked first keyword, to the second domain corpus. 19.The computer-implemented method of claim 16, wherein a keywordcollection ranking comprises taxonomy tag data associated with ataxonomy tag.
 20. The computer-implemented method of claim 16, furthercomprising: generating, by the device, a stochastic gradient descentvalue related to the trained model.